Reproducible Report Template - Basic
Project 1
Sam Higgs | 4404 5555 8, Anthony Simmon | 480383930, Ivar Orn Kristjansson | 480539753
University of Sydney | Data1001 | August 2018
1 Executive Summary
The aim of this report is to enable our client to make better decisions about stocking and marketing his liquor store. The aim of this report was to find out what kind of correlations are between weather and alcohol sales in the liquor store. We used the business’s sales data over the past 12 months collected using POS software along with teamperature and rainfall data from the Bureau of Meteorology to determine how weather effects sales so the client can use weather predictions to inform the products he stocks and markets. We chose to look at the effect temperature and rainfall have on sales as well as how temperature effects sales throughout the day.
The datasets were not clean and required alot of wrangling to clean. We had to strip columns, tokenize columns and perform type conversaions as well as append datasets both vertically and horizontally.
2 Initial Data Analysis (IDA)
2.1 Source of Data
This report combines sales data from an off-premise liquor store and weather data from the Bureau of Meteorology. The liquor store is called The Drink Hive (http://www.thedrinkhive.com.au/). It is located in a small up-market shopping centre called Saporium in Rosebery, NSW Australia. The sales data is primary data source pulled straight from the POS software Vend (https://www.vendhq.com/). It quantitatively measures time of sale, number of items sold and price whilst also measuring categories such as staff-member and product type. This allows us to objective measurements assuming that all transactions have been processed (which we have been assured have). We acknowledge that this is a small sample and does not necessarily represent the trends of the Australian consumer. The limitation of this data is that it only reflects one store and the trends of customers to that store.
The second dataset is weather data obtained from from the Bureau of Meteorology (BOM) (http://www.bom.gov.au/climate/data/) website. We chose BOM as they provide the most accurate and detailed quantitative measure of temperature per day available in Australia. This secondary data is openly available on their website. They are the primary source of this data and we trust that this data is accurate. Possible issues we may have with this data include temperature variations within one day. Because transactions occur at different periods throughout a day, and there are often fluctuations in temperature throughout a day, it may alter the results.
2.1.1 Rosebery Demographic
Rosebery has a population of approximately 10,000 with a median age of 33 and 2,500 families. The median weekly income is $1,900 and weekly rent is $580. 34% of residents have a university or tertiary education which is twice that of NSW average and 67% work full-time. 58% of the population live in flats or apartments. The suburb has a diverse mix of cultures with Chinese and Greek ancestory heavily over-represented compared to the NSW Average. 63% of the population’s parents were both born overseas, just under double the NSW average. Eastern orthodox over-represented and Anglican under-represented.(ABS, 2016)
2.1.2 Data Wrangling
The below script, written by Sindri, was used to clean and combine the two datasets. The files were only available in monthly increments so they were appended with any duplicate sales. The temperature and rainfall data was then joined with the sales data and it was exported in csv form.
cat(readLines('ProcessData.py'), sep='\n')
# coding: utf-8
import pandas as pd
# The path to all the sales data files
salesFiles = ['sales_39832.csv',
'sales_73522.csv',
'sales_40067.csv',
'sales_50466.csv',
'sales_74172.csv',
'sales_88136.csv',
'sales_90146.csv',
'sales_51323.csv',
'sales_43914.csv',
'sales_15816.csv',
'sales_32359.csv',
'sales_31902.csv',
'sales_29597.csv']
# Joining up the sales data into one file
Sales = pd.DataFrame()
count = 0
for filename in salesFiles:
name = 'data/' + filename
temp = pd.read_csv(name)
count += len(temp)
Sales = Sales.append(temp, ignore_index = True)
# Saving the sales combined sales file if we need it later
Sales.to_csv('data/TotalSales.csv')
# Splitting the time and date up into two columns to merge the Date column with the weather data
Sales['Time'] = Sales['Date'].apply(lambda x: x[11:])
Sales['Date'] = Sales['Date'].apply(lambda x: x[0:10])
# Joining up the temperature datasets from 2017 and 2018
temperature = pd.read_csv('data/2017_tmp_data.csv')
temp = pd.read_csv('data/2018_tmp_data.csv')
temperature = temperature.append(temp, ignore_index = True)
# Joining up the rainfall datasets from 2017 to 2018
rainfall = pd.read_csv('data/Rainfall_2017.csv')
temp = pd.read_csv('data/Rainfall_2018.csv')
rainfall = rainfall.append(temp, ignore_index = True)
# Combining the datasets on rainfall and temperature
weather = temperature.merge(rainfall, on=['Year', 'Month', 'Day'])[['Year', 'Month', 'Day', 'Maximum temperature (Degree C)', 'Rainfall amount (millimetres)']]
# The weather data had 3 columns for 'Year', 'Month' and 'Day'
# so they were joined into one column 'Date'
weather['Date'] = (weather['Year'].map(str) + '-'
+ weather['Month'].apply(lambda x: str(x).zfill(2)) + '-'
+ weather['Day'].apply(lambda x: str(x).zfill(2)))
# Joining up the weather data with the sales data
total = pd.merge(Sales.drop(['Customer Name', 'Customer Code', 'Note', 'Discount', 'AccountCodeSale', 'AccountCodePurchase', 'Register', 'User', 'Status', 'Sku', 'Line Type', 'Loyalty', 'Quantity', 'Subtotal', 'Sales Tax', 'Paid'], axis=1)
, weather.drop(['Year', 'Month', 'Day'], axis=1)
, on='Date'
, how = 'inner')
# Each transaction had multiple rows, one for each item, one for the total,
# one for any discount and an extra row if the payment was made by credit card.
# Also there were many duplicates. This cleans all this up into a single row
# retaining the total price.
idx = total.groupby(['Receipt Number'])['Total'].transform(max) == total['Total']
simplified = total[idx].drop_duplicates(subset = ['Date', 'Receipt Number', 'Total', 'Time'])
# Save the processed data
simplified.to_csv('data/ProcessedData.csv')2.2 Domain Knowledge
This report looks at the effect of weather on alcohol sales in the hope of improving the likelihood of small business success. A business’s core role is to provide goods and services. Their success is dependent on their ability to sell products for revenues. Researchers looking at Swedens distribution of SKU’s determined that it was assortment of products impacts sales (Friberg & Sanctuary, 2017). It is therefore imperative that alcohol retailers 1. Stock the correct product 2. Market the correct product 3. Price their products effectively
In the past, these decisions have been the intuition of a good business operator, however with the accessibility of data and analytics tools, we can now take a more scientific approach which can be replicated by any business.
There is a wealth of evidence which shows that weather influences consumer behaviour and understanding this leads to better marketing decisions (Murray et al., 2010). The key areas of consumer behaviour this report will address are what conditions weather effects consumer spending and what conditions effect consumer behaviour.
Weather is defined by Merriam Webster as the state of the atmosphere with respect to heat or cold, wetness or dryness, calm or storm, clearness or cloudiness. Researchers indicate that weather alters the shoppers mood which in turn influences their purchasing behaviour (Gaukler, 2010). They deduced that 1. Bad weather keeps people at home reducing foot traffic and sales 2. Weather can influence store traffic and sales volume, and 3. Weather can influence sales by affecting the customers internal state.
We will use this report to show small businesses how they can use their data to increase profitability by making informed decisions about stocking, marketing and pricing their products, helping them find the signal in the noise.
#This prevents strings from being factorised which keeps Date as Date
options(stringsAsFactors = FALSE)
# LOAD DATA
library(ggplot2)
library(tidyr)
library(xtable)
library(knitr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniondata = read.csv("data/ProcessedData.csv")
#Looking for data that is greater or equal to 1 dollar to remove any outlying sales
data <- data[data$Total >= 1,]
head(data$Date)## [1] "2018-08-06" "2018-08-06" "2018-08-06" "2018-08-06" "2018-08-06"
## [6] "2018-08-06"#Convert date to date format
# convert date column to date type and format to Australian date standards
data$Date <- format(as.Date(data$Date), "%d-%m-%Y")
# view R class of data
class(data$DATE)## [1] "NULL"## [1] "Date"
# view results
head(data$DATE)## NULL## [1] "2013-08-21" "2013-08-26" "2013-08-27" "2013-09-01" "2013-09-09"
## [6] "2013-09-10"
# Quick look at top 6 rows of data
head(data)## X Date Receipt.Number Total
## 1 0 06-08-2018 18179 27.00
## 2 4 06-08-2018 18178 24.00
## 3 7 06-08-2018 18177 47.00
## 4 11 06-08-2018 18176 27.00
## 5 16 06-08-2018 18175 31.05
## 6 21 06-08-2018 18174 33.99
## Details
## 1 1 X Cirillo Rose
## 2 2 X Fever Tree Elderflower Tonic 4pk
## 3 2 X Ps40 Smoked Lemonade + 1 X Athletes of Wine Vino Athletico Macedon Pinot noir
## 4 1 X Empty Wine Bottle 750ml + 1 X Unico Zelo Harvest Sauvignon Blanc KEG + -1 X Discount
## 5 3 X Frenchies Kolsch 330ml + 3 X Frenchies Comet Pale Ale 330ml + -1 X Discount
## 6 1 X Domaine Thomson - Explorer Pinot Noir
## Time Maximum.temperature..Degree.C. Rainfall.amount..millimetres.
## 1 18:42:40 19 0
## 2 18:03:54 19 0
## 3 17:45:58 19 0
## 4 17:32:56 19 0
## 5 16:26:31 19 0
## 6 15:05:09 19 0## Size of data
dim(data)## [1] 11853 8## R's classification of data
class(data)## [1] "data.frame"## R's classification of variables
str(data$Total)## num [1:11853] 27 24 47 27 31.1 ...2.3 Classification of Variables
The dataset contains 11853 sales. We are representing the population using a small sample described above. We have removed columns that are not relevant to our clients use. We are using a multivariant dataset that consists of 8 variables.
X, Date, Receipt.Number, Total, Details, Time, Maximum.temperature..Degree.C., Rainfall.amount..millimetres.
Each variable is classified at the beginning of the research question.
2.4 Stakeholders
2.4.1 Customer - Indirect External Stakeholder
2.4.2 Project Team - Direct Internal Stakeholder
Sam has a background in business and data analytics. He will focus on the Initial Data Analysis. He must be aware of the limitations of his understanding of data science to ensure the relevant variables are used. Anthony has a strong critical thinking mind and a background in scientific literature. He will focus on the evidence based conclusions, compilation and presentation of the report. He must be aware of his non-technical audience to present the findings in a way that can be understood by a small business owner. Sindri and Ivar have backgrounds in Computer Science. They will be combining and wrangling the datasets into a useable table and lead the data representations found in this report. They will be ensuring the numerical and graphical summaries are insightful and relevant. They must ensure the correct data points and graphical representations are used to portray the questions being researched.
We have a fantastic breadth of knowledge that will make this report a valuable and useful tool for Mal and other small business retail owners.
2.4.3 Sponsor - Primary & Secondary Stakeholder
Our tutor and the University of Sydney are our primary and Secondary stakeholder. They assist in our vision and are responsible for the overall success of the project. They will review our submission and have created specific outcomes that we will be measured against. We must be careful to meet the requirements for this stakeholder or they will fail us and we will not be able to continue to report and continue working.
2.5 Summary
3 Research Questions
3.1 RQ1: How does the maximum temperature effect transaction value?
3.1.1 Introduction
The amount spent per transaction is a useful indicator for retailers to determine how much stock to buy. If a retailer purchases to much stock, they may not have the cash flow to pay it back, to little and customers will not be able to buy the products they want.
We displayed this data using a bar graph to represent the average sale value in each temperature range. The value Inside each bar is the number of transactions. As you can see, the lower and upper temperature ranges have less transactions because there are less days.
We acknowledge that the limited number of transactions can skew the accuracy of the data in these ranges.
3.1.2 Classification of Variables
#The temperature is a quantitative variable. We start by changing it to a qualitative one using ranges that cover 5 degrees Celcius
temp = data$Maximum.temperature..Degree.C.
data$tempGroups = cut(temp, c(10,15,20,25,30,35,40,45))
#Take a look at overall data before looking at the graphs
heatData = data %>% drop_na(Maximum.temperature..Degree.C.)
dataFrame <- data.frame(Name = "Daily Max Temp",
Rows = c(nrow(data)-nrow(heatData)),
Class = c(class(temp)),
Min = c(min(temp)),
Max = c(max(temp)),
Mean = c(mean(temp)),
Median = c(median(temp)))
#Do the same for the sales data
sales = data$Total
salesData = data %>% drop_na(Total)
salesFrame <- data.frame(Name = "Sales",
Rows = c(nrow(data) - nrow(salesData)),
Class = c(class(temp)),
Min = c(min(sales)),
Max = c(max(sales)),
Mean = c(mean(sales)),
Median = c(median(sales))
)
newdf <- rbind(dataFrame, salesFrame)
kable(newdf, caption= "Variable Data Classification", col.names = c("Name"," Missing", "Type", "Minimum", "Maximum", "Mean", "Median"))| Name | Missing | Type | Minimum | Maximum | Mean | Median |
|---|---|---|---|---|---|---|
| Daily Max Temp | 0 | numeric | 14.30 | 43.4 | 23.28374 | 23.2 |
| Sales | 0 | numeric | 1.23 | 1920.0 | 54.49027 | 34.0 |
3.1.3 Graphs
We can see from the below bar graph that people seem to spend more money in milder temperature (15 - 40 degrees) but in more extreme temperatures (10 - 15 and 40 - 45 degrees) people tend to buy less alcohol. We can also observe a spike at the 35-40 degrees temperature range suggesting that people tend to spend more money per transaction when the temperature is between 35 and 40 degrees.
#Transaction sizes for each temperature range
meanPerPerson = aggregate(data$Total ~ data$tempGroups, data, mean)
medPerPerson = aggregate(data$Total ~ data$tempGroups, data, median)
transactions = merge(x = meanPerPerson, y = medPerPerson, by='data$tempGroups')
names(transactions) = c('Temperature', 'Mean_total', 'Median_total')
Fre <- as.data.frame(table(data$tempGroups))
colnames(Fre)[1] <- "tempGroups"
Fre$lab <- as.character(Fre$Freq)
#Barplot for average money spent with median lines
ggplot(transactions, aes(Temperature, Mean_total), label = Fre$Freq) + geom_bar(stat="identity", position = "dodge", fill = "#FF6666") + ggtitle("Average sale by temperature") + ylab("Sales (AUD)") + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) + geom_errorbar(data=transactions, aes(Temperature, ymax = Median_total, ymin = Median_total), size=1, linetype = "solid", inherit.aes = F, width = 0.9) + geom_text(aes(label = Fre$Freq), position = position_dodge(width = 0.9), vjust = 1.5) + scale_x_discrete(labels = c('10 - 15','15 - 20','20 - 25', '25 - 30', '30 - 35', '35 - 40', '40 - 45'))
The below graph looks at the total daily sales in different temperature ranges. We again see similar trends as above. That in extreme temperature (10 - 15 and 40 - 45 degrees) the store doesnât sell as much alcohol as in milder temperatures (15 - 40 degrees). We also see the same spike as above when the temperature is between 35 - 40 degrees.
#Total money spent for each temperature range
totalPerDay = aggregate(data$Total ~ data$tempGroups, data, sum)
nrOfDaysPerTemp = aggregate(data$Date ~ data$tempGroups, data, function(x) length(unique(x)))
totals = merge(x = totalPerDay, y = nrOfDaysPerTemp, by='data$tempGroups')
names(totals) = c('Temperature', 'Total', 'NrOfDays')
totals['meanPerDay'] = round(totals$Total / totals$NrOfDays, 1)
ggplot(totals, aes(Temperature, meanPerDay)) + geom_bar(stat="identity", position = "dodge") + geom_bar(stat="identity", position = "dodge", fill = "#56B4E9") + ggtitle("Average Daily Sales by Temperature") + ylab("Sales (AUD)") + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) + scale_x_discrete(labels = c('10 - 15','15 - 20','20 - 25', '25 - 30', '30 - 35', '35 - 40', '40 - 45'))
3.1.4 Summary
Looking at the values of the median and mean purchase transactions it becomes evident that there is not much change in the size of each transaction for the temperature ranges 15-30 degrees. The more extreme temperature values have more of an effect showing big drops in transaction sizes, however, it is worth noting that there were very few transactions during that time so the data might not accurately represent the consumer behaviour. The most noticeable spike in transaction sizes occurs at the 35-40 degree temperature range. This could be represented by people buying more alcohol at a time during the hot days of summer breaks when going travelling or having barbecues.
Looking at the data for the total money earned per day for the shop for the different temperature ranges we note that there is a steady increase in sales the hotter it gets from 20-40 degrees. It is also worth noting that even though each transaction is comparatively small for the 30-35 temperature range the total sales are high suggesting that for the hotter temperature people buy less at a time but more people buy alcohol.
3.2 RQ2: How does rainfall affect Sales?
3.2.1 Classification of Variables
rainData = data %>% drop_na(Rainfall.amount..millimetres.)
rain = rainData$Rainfall.amount..millimetres.
#We start by changing the rainfall from a quantitative variable to a qualitative one
rainData$rainGroups = cut(rain, c(0,0.1,15,70), include.lowest = TRUE)
#Transaction sizes for each temperature range
meanPerPerson = aggregate(rainData$Total ~ rainData$rainGroups, rainData, mean)
medPerPerson = aggregate(rainData$Total ~ rainData$rainGroups, data, median)
transactions = merge(x = meanPerPerson, y = medPerPerson, by='rainData$rainGroups')
names(transactions) = c('Rainfall', 'Mean_total', 'Median_total')
dataFrame <- data.frame(Rows = c(nrow(data)-nrow(rainData)),
Max = c(max(rain)),
Min = c(min(rain)),
Mean = c(mean(rain)),
Median = c(median(rain)))
kable(dataFrame, col.names = c("Missing rows", "Max rainfall", "Min rainfall", "Mean rainfall", "Median of rainfall"))| Missing rows | Max rainfall | Min rainfall | Mean rainfall | Median of rainfall |
|---|---|---|---|---|
| 74 | 69.4 | 0 | 1.667816 | 0 |
#Number of transactions for each rainfall range
ggplot(rainData, aes(rainGroups)) + geom_bar(fill = "#228B22") + scale_x_discrete(labels = c('No rain','Light rain', 'Heavy rain')) + ylab("Number of transactions") + xlab("Rainfall") + theme_bw() + ggtitle("Number of total transaction for different rainfall") + theme(plot.title = element_text(hjust = 0.5))
We decided to split up our rain data into 3 categories, no rain, light rain (0 - 15 mm) and heavy rain (15 - 70 mm) because as we can see from the bar plot we have a lot more of data points for no rain and light rain than heavy rain. So instead of making many categories for different rainfall we only made three for the most accurate results. We can also see from the table above there are a few missing rows. That can be explained by bad or missing values.
Let’s start by looking at how much a person spends in the store on average. As we can see from this bar plot there is little difference between these categories. People tend to spend a small amount more per purchase as the rainfall increases.
#Barplot for average money spent with median lines
ggplot(transactions, aes(Rainfall, Mean_total)) + geom_bar(stat="identity", position = "dodge", fill = "#FF6666") + ggtitle("Average money spent (in AUD) per purchase for different rainfall") + ylab("Sales (AUD)") + xlab("Rainfall") + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) + geom_errorbar(data=transactions, aes(Rainfall, ymax = Median_total, ymin = Median_total), size=1, linetype = "solid", inherit.aes = F, width = 0.9) + scale_x_discrete(labels = c('No rain','Light rain', 'heavy rain'))
Let’s look at what an average day looks like in total sales for rainfall in Sydney. This bar plot doesnât tell us the same story as the previous one. It is observable that the store has sold more alcohol when there has been light rain as we saw above but when it is raining heavily the store has sold less alcohol.
#Total money spent for each rainfall range
totalPerDay = aggregate(rainData$Total ~ rainData$rainGroups, rainData, sum)
nrOfDaysPerRain = aggregate(rainData$Date ~ rainData$rainGroups, rainData, function(x) length(unique(x)))
rainTotals = merge(x = totalPerDay, y = nrOfDaysPerRain, by='rainData$rainGroups')
names(rainTotals) = c('Rainfall', 'Total', 'NrOfDays')
rainTotals['meanPerDay'] = round(rainTotals$Total / rainTotals$NrOfDays, 1)
ggplot(rainTotals, aes(Rainfall, meanPerDay)) + geom_bar(stat="identity", position = "dodge", fill = "#56B4E9") + scale_x_discrete(labels = c('No rain','Light rain', 'Heavy rain')) + ylab("Sales (AUD)") + xlab("Rainfall") + theme_bw() + ggtitle("Average money spent (AUD) in one day for different rainfall") + theme(plot.title = element_text(hjust = 0.5))
Summary: When comparing these two graphs it becomes evident that there is a little trend in alcohol sales. When there have been days of light rain people have spent more per transaction and the store has sold more per day compared to days with no rain at all. We think that it might be because of when there is sunshine outside and good weather people are more likely to drink alcohol in a bar but when it is a day with light rain people are more likely to the shop to buy their alcohol. Another reason for this might be that on light rainy days people tend to do more grocery shopping and since the alcohol store is in a shopping centre itâs convenient for people to go also to the alcohol shop. However, if we take a look at days when it rains heavily people tend to spend more per transaction, but the store sells less per day. We think that might be because it rains so heavily that not many people go outside so fewer people go to the alcohol store but if people go outside they tend to buy more per transaction. In conclusion, people buy more per transaction when it rains but the store only generates more revenue when there are days with light rain rather than days with heavy rain or no rain at all.
3.3 RQ3: How does maximum temperature effect alcohol sales throughout the day?
3.3.1 Classification of Variables
hour = as.integer(substr(data$Time, 0, 2))
data$timeGroups = cut(hour, seq(8,20,1))
#table(data$timeGroups)
#Total money made per time gap
timeTemp = data %>%
group_by(timeGroups, tempGroups) %>%
summarize(total = sum(Total) )
names(nrOfDaysPerTemp) = c('tempGroups', 'nrOfDays')
timeTemp = merge(x= timeTemp, y=nrOfDaysPerTemp , on='tempGroups')
timeTemp['scaledTotal'] = timeTemp$total / timeTemp$nrOfDays
ggplot(timeTemp, aes(x = timeGroups, y = scaledTotal)) + geom_point(aes(color=tempGroups)) + geom_line(aes(color=tempGroups, group=tempGroups)) + scale_x_discrete(labels = c('8','9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19' , '20')) + ylab("Sales (AUD)") + xlab("Time of the day") + theme_bw() + ggtitle("Alcohol sales for different time of the day in relation to different temperature") + theme(plot.title = element_text(hjust = 0.5), legend.title=element_blank()) + scale_color_hue(labels = c("10 - 15", "15 - 20", "20 - 25", "25 - 30", "30 - 35", "35 - 40", "40 - 45"))
ggplot(timeTemp, aes(x = timeGroups, y = scaledTotal)) + geom_point(aes(color=tempGroups)) + facet_wrap(~tempGroups) + geom_line(aes(color=tempGroups, group=tempGroups)) + scale_x_discrete(labels = c('8','9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19' , '20')) + ylab("Sales (AUD)") + xlab("Time of the day") + theme_bw() + ggtitle("Alcohol sales for different time of the day in relation to different temperature") + theme(plot.title = element_text(hjust = 0.5), legend.position = "none")
#ggplot(test, aes(x = timeGroups, y = total)) + geom_point() + geom_line(aes(group=tempGroups))
#average money spent by customer per transaction, note
#testMean = data %>%
# group_by(timeGroups, tempGroups) %>%
# summarize(meanTotal = mean(Total) )
#ggplot(testMean, aes(x = timeGroups, y = meanTotal)) + geom_point() + facet_wrap(~tempGroups)Summary: The first trend worth noting is that for normal maximum temperature ranges (15-30 degrees) the overall sales over the day seem to follow a bell curve peaking at around 13-14 before dropping off again. For the coldest temperature range the peak is later in the day around 15-16 and for the hottest temperature ranges the peak in sales is earlier. For the maximum temperature of 35-40 degrees there is a very interesting massive spike in sales around noon however since there is not very much data for this temperature range we cannot draw any conclusions from it. For the more normal temperature ranges, 15-30 degrees, there is also a small but noticeable spike in sales in the late afternoon which might stem from people buying alcohol on their way home from work or before dinner.
4 Conclusions
Temperature, rainfall and time of day seem to have noticable effect on sales in a local alcohol store in Sydney. Temperature seems to have the most impact on sales of alcoholic drinks. People tend to buy more per purchase and the store sells more in milder temperatures (15 - 14 degrees) than more extreme temperatures (10 - 15 and 40 - 45 degrees). A noticable spike is also seen in both graphs for 35 - 40 degrees. Weather during summer break and holidays is often around that range which could explain the spike. Rainfall does not have as much noticable impact on sales as temperature did. Light rain seems to have more impact than heavy rain and no rain at all on sales of alcoholic drinks. People spend a most per transaction when there is heavy rain but the difference is though not much between the data sets. However, when there is light rain the store sells more on the average day with much more noticable difference.
5 References
Australian Bureau of Statistics, 2016, 2016 Census QuickStats, Rosebery. viewed 21/08/2018 http://quickstats.censusdata.abs.gov.au/census_services/getproduct/census/2016/quickstat/SSC13405?opendocument
Friberg, R., & Sanctuary, M. (2017). The effect of retail distribution on sales of alcoholic beverages. Marketing Science, 36(4), 626-641. doi:10.1287/mksc.2017.1038
Gaukler, G. M. (2010). Preventing avoidable stockouts: The impact of item-level RFID in retail. Journal of Business & Industrial Marketing, 25(8), 572-581. doi:10.1108/08858621011088301
Kyle B. Murray, Fabrizio Di Muro, Adam Finn, Peter Popkowski Leszczyc, (2010). The effect of weather on consumer spending. Journal of Retailing and Consumer Services (17) 512-520
Weather. (n.d) In Merriam-Websters Collegiate Dictionary. Retrieved from https://www.merriam-webster.com/dictionary/weather
6 Personal reflection on group work
- The way I contributed was
- What I learnt about group work was …